2024 Faculty Courses School of Engineering Undergraduate major in Electrical and Electronic Engineering
Electrical and Electronic Informatics II
- Academic unit or major
- Undergraduate major in Electrical and Electronic Engineering
- Instructor(s)
- Tomohiro Amemiya / Keigo Arai
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Tue / 7-8 Fri
- Class
- -
- Course Code
- EEE.M252
- Number of credits
- 200
- Course offered
- 2024
- Offered quarter
- 4Q
- Syllabus updated
- Mar 17, 2025
- Language
- Japanese
Syllabus
Course overview and goals
In addition to the machine learning methods learned in "EEE.M251: Electrical and Electronic Informatics I", this course (EEE.M252) covers various algorithms for deep learning. An understanding of the basic concepts of deep learning is important for future research and development in various fields of electrical and electronic systems.
Course description and aims
Students will acquire the following abilities.
1) Deepen understanding of various algorithms for deep learning.
2) To be able to code a simple neural network in Python.
Keywords
AI, deep learning, Python
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
- Applied professional capacity in electrical and electronic fields
Class flow
In each lecture, explanations will be given using specified slides.
In addition, there will be 2-3 guest lectures.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Introduction | Introduction |
| Class 2 | Concepts of Deep Learning | Deepen understanding of the overall overview of deep learn |
| Class 3 | Loss function | Learn about typical loss function used for deep networks |
| Class 4 | Back Propagation method | Learn about Back Propagation method. |
| Class 5 | Convolutional neural networks (CNN) | Learn the basics of convolutional neural networks for image data processing |
| Class 6 | Vanishing gradient problem and Convergence in deep learning I | Learn about vanishing gradient problem and convergence. |
| Class 7 | Vanishing gradient problem and Convergence in deep learning II | Learn about vanishing gradient problem and convergence. |
| Class 8 | Guest Lecture | Guest Lecture |
| Class 9 | Recurrent neural network | Learn about recursive neural networks for time series data processing |
| Class 10 | Attention mechanism and Transformer I | Explain the details of the Attention Mechanism and Transformer. |
| Class 11 | Attention mechanism and Transformer II | Explain the details of the Attention Mechanism and Transformer. |
| Class 12 | Guest Lecture | Guest Lecture |
| Class 13 | Generative model I | Learn about variational auto-encoder (VAE). |
| Class 14 | Generative model II | Learn about typical diffusion model. |
| Class 15 | Guest Lecture | Guest Lecture |
Study advice (preparation and review)
Basically, no preparation is required, but it is strongly recommended to read through the relevant sections of reference books to deepen your understanding.
Textbook(s)
In each lecture, explanations will be given using specified slides.
Reference books, course materials, etc.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (O'Reilly)
深層学習 改訂第2版 (機械学習プロフェッショナルシリーズ)（講談社）(in Japanese)
拡散モデル データ生成技術の数理（岩波書店）(in Japanese)
Evaluation methods and criteria
Grades will be assigned as follows.
Attendance report (40%)
Midterm: Submission of Python code (30%)
Final exam (30%)
Related courses
- EEE.M221 ： Computation Algorithms and Programming
- EEE.M231 ： Applied Probability and Statistical Theory
- EEE.M251 ： Electrical and Electronic Informatics I
Prerequisites
Just a little motivation and passion
EEE.M251 Electrical and Electronic Informatics I : Near required
EEE.M221 Computation Algorithms and Programming : Recommended
EEE.M231 Applied Probability and Statistical Theory : Recommended